1. Oracle Advanced Analytics
PRODUCT
Oracle Data Miner & Oracle Enterprise R
Data mining, machine learning & data science in general are primarily algorithms.
Open source R, in addition, is a programming language, primarily made for statistics, but it is a language and does much more.
ORE is R that can run in the database and without data volume limitations.
Both exist for the Oracle DB EE and for the BDA.

2. Oracle data mining algorithms (2/7):
Technique
Application areas
Algorithm(s)
CLASSIFICATION
Most commonly used technique for predicting a specific outcome such as response / no-response, high / medium / low-value customer, likely to buy / not buy.
Logistic Regression —classic statistical technique but now available inside the Oracle Database and supports text and transactional data
Naive Bayes —Fast, simple, commonly applicable Support Vector Machine—Next generation, supports text and wide data Decision Tree —Popular, provides human-readable rules
REGRESSION
Technique for predicting a continuous numerical outcome such as customer lifetime value, house value, process yield rates.
Multiple Regression —classic statistical technique but now available inside the Oracle Database and supports text and transactional data
Support Vector Machine —Next generation, supports text and wide data

3. Oracle data mining algorithms (4/7):
Technique
Application areas
Algorithm(s)
ATTRIBUTE IMPORTANCE
Ranks attributes according to strength of relationship with target attribute. Use cases include finding factors most associated with customers who respond to an offer, factors most associated with healthy patients.
Minimum Description Length—Considers each attribute as a simple predictive model of the target class
ANOMALY DETECTION
Identifies unusual or suspicious cases based on deviation from the norm. Common examples include health care fraud, expense report fraud, and tax compliance. 
One-Class Support Vector Machine —Trains on "normal" cases to flag unusual cases

4. Oracle data mining algorithms (6/7):
Technique
Application areas
Algorithm(s)
CLUSTERING
Useful for exploring data and finding natural groupings. Members of a cluster are more like each other than they are like members of a different cluster. Common examples include finding new customer segments, and life sciences discovery.
Enhanced K-Means—Supports text mining, hierarchical clustering, distance based Orthogonal Partitioning Clustering—Hierarchical clustering, density based Expectation Maximization—Clustering technique that performs well in mixed data (dense and sparse) data mining problems.
ASSOCIATION
Finds rules associated with frequently co-occurring items, used for market basket analysis, cross-sell, root cause analysis. Useful for product bundling, in-store placement, and defect analysis.
Apriori—Industry standard for market basket analysis

5. Oracle data mining algorithms (7/7):
Technique
Application areas
Algorithm(s)
FEATURE SELECTION and EXTRACTION
Produces new attributes as linear combination of existing attributes. Applicable for text data, latent semantic analysis, data compression, data decomposition and projection, and pattern recognition.
Non-negative Matrix Factorization—Next generation, maps the original data into the new set of attributes Principal Components Analysis (PCA)—creates new fewer composite attributes that respresent all the attributes. Singular Vector Decomposition—established feature extraction method that has a wide range of applications.

6. Oracle text mining (x IBM Watson)
Sentiment analysis on social media, document- centric and text-centric analyses etc. all use techniques based on analysis of text.
And there is this strange myth that says that Oracle does not have text analytics. Take a look at these:
Oracle Text on 12c:
Oracle text mining:
Tutorial (text mining with Oracle Data Miner): od/bidw/datamining/ODM11gR2-TextMining.htm
Table 20 – Oracle Data Mining Algorithms that Support Text
Algorithm
Mining Function
Naïve Bayes
Classification
Generalized Linear Models
Classification. Regression
Support Vector Machine
Classification, Regression, Anomaly Detection
k-Means
Clustering
Non-Negative Matrix Factorization
Feature Extraction
Apriori
Association Rules
Minimum Descriptor Length
Attribute Importance

7. R in R Studio (screen capture):
PREDICT FAILURE!